More Than Binary: Inclusive Gender Collection and You

Excerpt

Many people identify their gender in many ways. So why do we build systems to capture accurate gender information with a dropdown that only lists “male” and “female”?
This talk covers why you might want to consider alternative ways of selecting gender for your users, a brief overview of the current best practices, the case study of the decisions I made when creating my open source project Gender Amender (a library you can help work on right now!), and why more work needs to be done. I'd also like to facilitate a short discussion during the time slot, so that we can share varied perspectives on how to improve the entire process of gender collection, and articulate the lenses through which we can and should view gender (e.g. “what are some other data structures we could use to capture gender identity information?”).

Description

At Meetup, one of the main ways we make our platform personal is by targeting recommendations based on gender. Unfortunately, the gender selection on our platform at the time I joined (though no longer) was a single dropdown, with options for “Male,” “Female,” and “Other.” If you didn’t identify in accordance with one of those, not only did we not target recommendations effectively for you, but we probably misgendered you – not exactly a friendly or personal experience. How could we, and other organizations, change that?

Current practices

Most forms which require you to select a gender require you to select only one option from a list of two or, if you’re lucky, three or four. There are a few notable exceptions to this. Facebook, if you select “Custom,” allows you to enter text, and attempts to suggest matches to what you’ve typed so far beneath the text field. OKCupid allows you to select up to five gendering words from a list of their curation.

#genderUX and why it’s not enough; what current best practices leave out

In many situations, the current best practice is to provide some sort of freeform input: http://43epnd.axshare.com/gender.html. If your only focus is collecting accurate gender data, there’s an argument that this is the only way to ensure that everyone has a fair shot at representation: https://modelviewculture.com/pieces/the-argument-for-free-form-input. Many thinkers, including previous OSBridge speakers, have also suggested tagging systems: http://opensourcebridge.org/wiki/2015/Male-Female-Othered%3A_Implementing_Gender-Inclusiveness_in_User_Data_Collection
I think that freeform input is great, in theory, but what happens to the freeform input once it’s collected? If you are Facebook, maybe you have enough resources to build a natural language processing system to determine which genders people might have from what they say about themselves (though they don’t do this). Otherwise, developers don’t have enough guidance to make freeform data useful, and can only ignore it. A tagging system like OKCupid’s is also a step in the right direction, but it’s limited by the tags that developers come up with – a potential member still might not see themselves.
The UX portion of genderUX is important, but I feel that collecting data that can be handled in an inclusive way once it reaches backend developers is as important to the user experience as what the field looks like.
After several discussions, and a survey of existing inclusive gender options, I created my own library to try and bridge this gap. Gender Amender (https://github.com/anne-decusatis/genderamender) uses JavaScript and Python in order to maintain a list of relevant gender options for a member of your site to select from, and knows enough about each of the genders submitted that no natural language processing is necessary.

Why collect gender at all, if it’s so hard to do?

The short answer: You probably don’t have to. But if you value knowing about the genders of your users, hopefully getting accurate information about them will also be valuable to you.

What Gender Amender is not for; why we still need other solutions in this space

Gender Amender is best implemented where it’s not prohibitively difficult to provide typing input, and is only suitable for systems which provide feedback in real time. Gender Amender is great for web forms! Gender Amender is not great for text messaging.

Next steps

I’m looking for help both in the form of contributions to Gender Amender and in the form of wider discussion in the community about gender collection specifically and privacy and information collection generally.

Discussion

I want to ask attendees the following questions:
1. What stops you from implementing inclusive gender collection systems in your work and hobby projects? What help do you need?
2. If you could pick a new lens to capture and view gender information through, what would you consider important in the decision? What would you choose?

Tags

gender, user experience, data collection, data processing

Speaking experience

I will be giving this talk at PyCon 2016, June 1 in Portland (https://us.pycon.org/2016/schedule/presentation/2023/), as well as at a local meetup at home in NYC, QueensJS, May 4 2016.

Additional experience:
I will be giving an unrelated talk at !!con this year, May 2016!
I've also spoken before at World Maker Faire 2015: http://anne.loves.technology/slides/wmf15/#0
I've been on a couple panels: I moderated a career panel at Women Who Code NYC, March 2016, and I spoke on panel at Meetup's tech crawl as a part of Lesbians Who Tech NYC , October 2015.
I presented a poster at the 2014 Grace Hopper Celebration of Women in Computing.
I've also run volunteer workshops, such as some introductory Raspberry Pi workshops for middle school students in 2015.

Speaker

Biography

Anne DeCusatis is currently a Core Engineer at Meetup. Before that, she was the December 2014 Outstanding Graduate for Computer Science at the State University of New York at New Paltz, and an intern with IBM WebSphere Test. In her spare time, she organizes MergeSort NYC, a feminist hackerspace.

Sessions

Many people identify their gender in many ways. So why do we build systems to capture accurate gender information with a dropdown that only lists “male” and “female”?
This talk covers why you might want to consider alternative ways of selecting gender for your users, a brief overview of the current best practices, the case study of the decisions I made when creating my open source project Gender Amender (a library you can help work on right now!), and why more work needs to be done. I’d also like to facilitate a short discussion during the time slot, so that we can share varied perspectives on how to improve the entire process of gender collection, and articulate the lenses through which we can and should view gender (e.g. “what are some other data structures we could use to capture gender identity information?”).